A Highly Configurable Flexible Analytic Model for MCI/Frailty Risk Detection in Age-friendly Cities
Апстракт
The global human population is aging rapidly, however, living longer does not necessarily mean living healthy, active and independent life. The emerging disruptive technologies like the Internet of Things (IoT) are proving instrumental in addressing this prominent societal challenge. Urban IoT infrastructures, designed to support the Smart City vision, enable capturing of personal data for analyzing behaviour of elderly people. Activities within the Horizon 2020 City4Age project are aimed at showing that behavioral analysis can help detect and mitigate risks of Mild Cognitive Impairment (MCI) and frailty problems of elderly. This paper presents the latest developments in extending the configurability and flexibility of the comprehensive City4Age computational model for risk detection. The proposed model extensions have demonstrated seamless adaptation to specific characteristics of various urban contexts, as well as seamless "pluggable" integration of various combined evolving and exte...ndable parameterized algorithm implementations and methods for behaviour variation and risk recognition, based on relevant statistical and machine learning techniques.
Кључне речи:
temporal analysis / geriatric model / flexible modelling / data-driven development / configurabilityИзвор:
2018 26th International Conference on Software, Telecommunications and Computer Networks (Softcom), 2018, 129-134Издавач:
- IEEE, New York
Финансирање / пројекти:
- Elderly-friendly City services for active and healthy ageing (EU-H2020-689731)
Институција/група
Fakultet organizacionih naukaTY - CONF AU - Urošević, Vladimir AU - Andrić, Marina AU - Vukićević, Milan AU - Tatsiopoulos, Christos PY - 2018 UR - https://rfos.fon.bg.ac.rs/handle/123456789/1793 AB - The global human population is aging rapidly, however, living longer does not necessarily mean living healthy, active and independent life. The emerging disruptive technologies like the Internet of Things (IoT) are proving instrumental in addressing this prominent societal challenge. Urban IoT infrastructures, designed to support the Smart City vision, enable capturing of personal data for analyzing behaviour of elderly people. Activities within the Horizon 2020 City4Age project are aimed at showing that behavioral analysis can help detect and mitigate risks of Mild Cognitive Impairment (MCI) and frailty problems of elderly. This paper presents the latest developments in extending the configurability and flexibility of the comprehensive City4Age computational model for risk detection. The proposed model extensions have demonstrated seamless adaptation to specific characteristics of various urban contexts, as well as seamless "pluggable" integration of various combined evolving and extendable parameterized algorithm implementations and methods for behaviour variation and risk recognition, based on relevant statistical and machine learning techniques. PB - IEEE, New York C3 - 2018 26th International Conference on Software, Telecommunications and Computer Networks (Softcom) T1 - A Highly Configurable Flexible Analytic Model for MCI/Frailty Risk Detection in Age-friendly Cities EP - 134 SP - 129 UR - conv_2136 ER -
@conference{ author = "Urošević, Vladimir and Andrić, Marina and Vukićević, Milan and Tatsiopoulos, Christos", year = "2018", abstract = "The global human population is aging rapidly, however, living longer does not necessarily mean living healthy, active and independent life. The emerging disruptive technologies like the Internet of Things (IoT) are proving instrumental in addressing this prominent societal challenge. Urban IoT infrastructures, designed to support the Smart City vision, enable capturing of personal data for analyzing behaviour of elderly people. Activities within the Horizon 2020 City4Age project are aimed at showing that behavioral analysis can help detect and mitigate risks of Mild Cognitive Impairment (MCI) and frailty problems of elderly. This paper presents the latest developments in extending the configurability and flexibility of the comprehensive City4Age computational model for risk detection. The proposed model extensions have demonstrated seamless adaptation to specific characteristics of various urban contexts, as well as seamless "pluggable" integration of various combined evolving and extendable parameterized algorithm implementations and methods for behaviour variation and risk recognition, based on relevant statistical and machine learning techniques.", publisher = "IEEE, New York", journal = "2018 26th International Conference on Software, Telecommunications and Computer Networks (Softcom)", title = "A Highly Configurable Flexible Analytic Model for MCI/Frailty Risk Detection in Age-friendly Cities", pages = "134-129", url = "conv_2136" }
Urošević, V., Andrić, M., Vukićević, M.,& Tatsiopoulos, C.. (2018). A Highly Configurable Flexible Analytic Model for MCI/Frailty Risk Detection in Age-friendly Cities. in 2018 26th International Conference on Software, Telecommunications and Computer Networks (Softcom) IEEE, New York., 129-134. conv_2136
Urošević V, Andrić M, Vukićević M, Tatsiopoulos C. A Highly Configurable Flexible Analytic Model for MCI/Frailty Risk Detection in Age-friendly Cities. in 2018 26th International Conference on Software, Telecommunications and Computer Networks (Softcom). 2018;:129-134. conv_2136 .
Urošević, Vladimir, Andrić, Marina, Vukićević, Milan, Tatsiopoulos, Christos, "A Highly Configurable Flexible Analytic Model for MCI/Frailty Risk Detection in Age-friendly Cities" in 2018 26th International Conference on Software, Telecommunications and Computer Networks (Softcom) (2018):129-134, conv_2136 .